Rashid.A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty[J]. Chemical Engineering Science .2013...
As a special case of ridge regression, correlation filters generate their training set {xi|i = 0, . . . , l−1} by cyclically shifting a base sample, x ∈ Rl, such that xi = Pilx, where Pl is the permutation matrix of l × l [26], and the yis are often Gaussian labels. ...
(1) Design matrices for CpG sites, mRNAs, and miRNAs are aggregated to form a linear or Gaussian kernel matrix that measures the similarity of samples. (2) Clinical variables are regressed out of the outcomes IQ and SRS and from the omic kernels to limit influence from these variables. (3...
CEEMD by adding two Gaussian white noise signals with opposite values to the original signal, which are then subjected to separate EMD decompositions. In ensuring that the decomposition effect is comparable to that of EEMD, CEEMD reduces the reconstruction error induced by the EEMD method. After th...
Gaussian processesHuman motionMatrix decompositionMulti-task learningUnsupervised learningVariational BayesIn this work, we propose a novel method for rectifying damaged motion sequences in an unsupervised manner. In order to achieve maximal accuracy, the proposed model takes advantage of three key prop.....
Gaussian processesRegressionAtrophyBSIMulti-kernel learningMRIPET Alzheimer’s diseaseMild cognitive impairmentMachine learning approaches have had some success in predicting conversion to Alzheimer's Disease (AD) in subjects with mild cognitive impairment (MCI), a less serious condition that nonetheless is...
multi-output Gaussian process regressioncombined kernel functionpolyester esterification processIn polyester fiber industrial processes,the prediction of key performance indicators is vital for product quality.The esterification process is an indispensable step in the polyester polymerization process.It has the ...
The proposed framework utilizes a set of kernel modeled Gaussian processes where each one is equipped with a different kernel function. The proposed method is applied for prediction making on a set of electric load patterns and provides high accuracy as compared to single Gaussian process models....
Support vector machineMulti task learningGaussian processesKernel learningThis paper proposes a novel way to learn multi-task kernel machines by combining the structure of classical Support Vector Machine (SVM) optimization problem with multi-task covariance functions developed in Gaussian process (GP) ...
Deep Gaussian Processes (DGPs) were proposed as an expressive Bayesian model capable of a mathematically grounded estimation of uncertainty. The expressivity of DPGs results from not only the compositional character but the distribution propagation within the hierarchy. Recently, it was pointed out that...